{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "65214853-d078-4fe6-950a-b9f7ccf6cb89",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cantera as ct\n",
    "import scipy\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import Video\n",
    "from IPython.display import HTML\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "143c8c1f-10d0-4a59-a77d-5649f733c504",
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameter values\n",
    "p0 = 101325  # pressure\n",
    "T0 = 296  # inlet temperature\n",
    "phi = 0.7 # equivalence ratio\n",
    "alpha = 0.566 # volume fraction(<1)\n",
    "\n",
    "#mdot(=mass flux, 단위 면적당 시간당 전달되는 질량)\n",
    "mdot_reactants = 80  # kg/m^2/s # 반응물이 1초당 1m² 면적을 통해 80kg 만큼 들어온다\n",
    "mdot_products = mdot_reactants  # kg/m^2/s #생성물이 반응 후에 같은 속도로 배출된다, mass conservation\n",
    "\n",
    "# Mechanism and fuel composition\n",
    "xh2 = alpha / (1 - alpha) * 1  # xh2 = alpha/(1-alpha)*xc\n",
    "fuel = {'CH4': 1, 'H2': xh2}\n",
    "oxidizer = {'O2': 1, 'N2': 3.76}\n",
    "\n",
    "# Calculation params\n",
    "width = 0.1  # m domain width\n",
    "loglevel = 0  # amount of diagnostic output (0 to 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "9aedca9b-1e11-48b4-ad69-1ee931fa956c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1882.0048963720276\n"
     ]
    }
   ],
   "source": [
    "# Gas object 1 for the counterflow flame\n",
    "gas1=ct.Solution('gri30.yaml') # reaction mechanism file\n",
    "gas1.TP = T0, p0\n",
    "\n",
    "#fix the composition of the fuel\n",
    "gas1.set_equivalence_ratio(phi, fuel, oxidizer)  # hold temperature and pressure constant \n",
    "\n",
    "# Create the counterflow premixed flame simulation object\n",
    "fl1 = ct.CounterflowPremixedFlame(gas=gas1, width=width)\n",
    "fl1.transport_model = 'multicomponent'\n",
    "fl1.energy_enabled = True #energy equation\n",
    "fl1.set_refine_criteria(ratio=3, slope=0.1, curve=0.2, prune=0.02)\n",
    "\n",
    "# set the boundary flow rates\n",
    "fl1.reactants.mdot = mdot_reactants\n",
    "fl1.products.mdot = mdot_products\n",
    "\n",
    "fl1.set_initial_guess()  # assume adiabatic equilibrium products\n",
    "fl1.solve(loglevel, auto=True)\n",
    "print(fl1.products.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "1a5708cc-9920-45a0-8ca8-0e92adbd4642",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000.0\n"
     ]
    }
   ],
   "source": [
    "# Gas object 2 for the counterflow flame with non equilibrium products\n",
    "gas2=ct.Solution('gri30.yaml')\n",
    "gas2.TP = T0, p0\n",
    "gas2.set_equivalence_ratio(phi, fuel, oxidizer)\n",
    "\n",
    "# Create the flame simulation object\n",
    "fl2 = ct.CounterflowPremixedFlame(gas=gas2, width=width)\n",
    "# Set grid refinement parameters\n",
    "fl2.set_refine_criteria(ratio=3, slope=0.1, curve=0.2, prune=0.02)\n",
    "\n",
    "# set the boundary flow rates\n",
    "fl2.reactants.mdot = mdot_reactants\n",
    "fl2.products.mdot = mdot_products\n",
    "\n",
    "#product temperature, temperature of my project\n",
    "fl2.products.T =2000\n",
    "fl2.products.X =fl1.products.X #composition of the products\n",
    "\n",
    "fl2.set_initial_guess(equilibrate=False)\n",
    "fl2.solve(loglevel, auto=True)\n",
    "print(fl2.products.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "458e8f4e-817b-43cc-8f80-1fc0925e3df9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x302ad0790>]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(fl1.grid,fl1.velocity)\n",
    "plt.axhline(y=0, color='k', linestyle='--',linewidth=1) #stagnation line\n",
    "#plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(fl1.grid,fl1.T)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "317d3aef-42e2-4bf0-a5e2-839349cc553b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x302dc1310>]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(fl2.grid,fl2.velocity)\n",
    "plt.axhline(y=0, color='k', linestyle='--',linewidth=1) #stagnation line\n",
    "#plt.show()\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(fl2.grid,fl2.T)\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "26061d26-0ef2-47a8-96f8-a9ab9874b506",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "consumption speed (Sc) for fl1: 0.13581276587495672 m/s\n",
      "consumption speed (Sc) for fl2: 0.1533183922293594 m/s\n"
     ]
    }
   ],
   "source": [
    "def calculate_consumption_speed(flame, species):\n",
    "    \"\"\"\n",
    "    function that calculate consumption speed about the flame and species\n",
    "    \n",
    "    Parameters:\n",
    "    flame (ct.CounterflowPremixedFlame): flame object\n",
    "    species (str): species that calculate consumption speed(ex: 'CH4')\n",
    "    \n",
    "    Returns:\n",
    "    float: consumption speed (m/s)\n",
    "    \"\"\"\n",
    "    omega_f = flame.net_production_rates  # 화학 반응 속도 (모든 종에 대해)\n",
    "    rho_u = flame.density[0]  # 반응물 밀도\n",
    "    Y_f_u = flame.Y[0, flame.gas.species_index(species)]  # reactants mass fraction\n",
    "\n",
    "    # select react speed about the certain species\n",
    "    omega_f_species = omega_f[flame.gas.species_index(species), :]  # (grid size,) 형태로 가져옴\n",
    "\n",
    "    # distance difference in grid\n",
    "    dx = np.diff(flame.grid)  # (크기: n_grid_points-1)\n",
    "\n",
    "    # using omega_f average between two grids to integrate\n",
    "    omega_avg = 0.5 * (omega_f_species[:-1] + omega_f_species[1:])  # avg btn grids\n",
    "\n",
    "    # integrate\n",
    "    integral_omega_f = np.sum(omega_avg * dx)  # 적분\n",
    "\n",
    "    # Sc calculation\n",
    "    S_cf = -1 / (rho_u * Y_f_u) * integral_omega_f\n",
    "    return S_cf\n",
    "\n",
    "# fl1 consumption speed\n",
    "Sc_fl1 = calculate_consumption_speed(fl1, 'CH4')\n",
    "print(f'consumption speed (Sc) for fl1: {Sc_fl1} m/s')\n",
    "\n",
    "# fl2 consumption speed\n",
    "Sc_fl2 = calculate_consumption_speed(fl2, 'CH4')\n",
    "print(f'consumption speed (Sc) for fl2: {Sc_fl2} m/s')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "27916bdb-0936-488a-9161-e2f2fc0d176c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "point 0: temperature 296.00 K, heat loss -0.40 W/m^2\n",
      "point 1: temperature 296.00 K, heat loss -0.40 W/m^2\n",
      "point 2: temperature 296.00 K, heat loss -0.40 W/m^2\n",
      "point 3: temperature 296.00 K, heat loss -0.40 W/m^2\n",
      "point 4: temperature 296.00 K, heat loss -0.40 W/m^2\n",
      "point 5: temperature 296.00 K, heat loss -0.40 W/m^2\n",
      "point 6: temperature 296.00 K, heat loss -0.40 W/m^2\n",
      "point 7: temperature 296.00 K, heat loss -0.40 W/m^2\n",
      "point 8: temperature 296.00 K, heat loss -0.40 W/m^2\n",
      "point 9: temperature 296.00 K, heat loss -0.40 W/m^2\n",
      "point 10: temperature 296.00 K, heat loss -0.40 W/m^2\n",
      "point 11: temperature 296.00 K, heat loss -0.40 W/m^2\n",
      "point 12: temperature 296.00 K, heat loss -0.40 W/m^2\n",
      "point 13: temperature 296.00 K, heat loss -0.40 W/m^2\n",
      "point 14: temperature 296.00 K, heat loss -0.40 W/m^2\n",
      "point 15: temperature 296.00 K, heat loss -0.40 W/m^2\n",
      "point 16: temperature 296.00 K, heat loss -0.40 W/m^2\n",
      "point 17: temperature 296.00 K, heat loss -0.40 W/m^2\n",
      "point 18: temperature 296.00 K, heat loss -0.40 W/m^2\n",
      "point 19: temperature 296.01 K, heat loss -0.40 W/m^2\n",
      "point 20: temperature 296.04 K, heat loss -0.40 W/m^2\n",
      "point 21: temperature 296.11 K, heat loss -0.39 W/m^2\n",
      "point 22: temperature 296.32 K, heat loss -0.37 W/m^2\n",
      "point 23: temperature 296.93 K, heat loss -0.31 W/m^2\n",
      "point 24: temperature 298.54 K, heat loss -0.15 W/m^2\n",
      "point 25: temperature 302.55 K, heat loss 0.25 W/m^2\n",
      "point 26: temperature 306.52 K, heat loss 0.65 W/m^2\n",
      "point 27: temperature 312.87 K, heat loss 1.29 W/m^2\n",
      "point 28: temperature 322.68 K, heat loss 2.27 W/m^2\n",
      "point 29: temperature 329.39 K, heat loss 2.94 W/m^2\n",
      "point 30: temperature 337.62 K, heat loss 3.76 W/m^2\n",
      "point 31: temperature 347.60 K, heat loss 4.76 W/m^2\n",
      "point 32: temperature 359.50 K, heat loss 5.95 W/m^2\n",
      "point 33: temperature 373.51 K, heat loss 7.35 W/m^2\n",
      "point 34: temperature 389.77 K, heat loss 8.98 W/m^2\n",
      "point 35: temperature 398.78 K, heat loss 9.88 W/m^2\n",
      "point 36: temperature 408.41 K, heat loss 10.84 W/m^2\n",
      "point 37: temperature 418.67 K, heat loss 11.87 W/m^2\n",
      "point 38: temperature 429.54 K, heat loss 12.95 W/m^2\n",
      "point 39: temperature 441.02 K, heat loss 14.10 W/m^2\n",
      "point 40: temperature 453.12 K, heat loss 15.31 W/m^2\n",
      "point 41: temperature 465.81 K, heat loss 16.58 W/m^2\n",
      "point 42: temperature 479.08 K, heat loss 17.91 W/m^2\n",
      "point 43: temperature 507.30 K, heat loss 20.73 W/m^2\n",
      "point 44: temperature 537.55 K, heat loss 23.75 W/m^2\n",
      "point 45: temperature 569.65 K, heat loss 26.96 W/m^2\n",
      "point 46: temperature 603.39 K, heat loss 30.34 W/m^2\n",
      "point 47: temperature 638.56 K, heat loss 33.86 W/m^2\n",
      "point 48: temperature 674.94 K, heat loss 37.49 W/m^2\n",
      "point 49: temperature 712.30 K, heat loss 41.23 W/m^2\n",
      "point 50: temperature 750.45 K, heat loss 45.04 W/m^2\n",
      "point 51: temperature 789.17 K, heat loss 48.92 W/m^2\n",
      "point 52: temperature 828.31 K, heat loss 52.83 W/m^2\n",
      "point 53: temperature 867.68 K, heat loss 56.77 W/m^2\n",
      "point 54: temperature 907.15 K, heat loss 60.72 W/m^2\n",
      "point 55: temperature 946.58 K, heat loss 64.66 W/m^2\n",
      "point 56: temperature 985.85 K, heat loss 68.59 W/m^2\n",
      "point 57: temperature 1024.84 K, heat loss 72.48 W/m^2\n",
      "point 58: temperature 1063.44 K, heat loss 76.34 W/m^2\n",
      "point 59: temperature 1101.51 K, heat loss 80.15 W/m^2\n",
      "point 60: temperature 1138.94 K, heat loss 83.89 W/m^2\n",
      "point 61: temperature 1175.60 K, heat loss 87.56 W/m^2\n",
      "point 62: temperature 1211.38 K, heat loss 91.14 W/m^2\n",
      "point 63: temperature 1246.17 K, heat loss 94.62 W/m^2\n",
      "point 64: temperature 1279.87 K, heat loss 97.99 W/m^2\n",
      "point 65: temperature 1312.38 K, heat loss 101.24 W/m^2\n",
      "point 66: temperature 1343.61 K, heat loss 104.36 W/m^2\n",
      "point 67: temperature 1373.51 K, heat loss 107.35 W/m^2\n",
      "point 68: temperature 1402.00 K, heat loss 110.20 W/m^2\n",
      "point 69: temperature 1429.06 K, heat loss 112.91 W/m^2\n",
      "point 70: temperature 1454.67 K, heat loss 115.47 W/m^2\n",
      "point 71: temperature 1478.81 K, heat loss 117.88 W/m^2\n",
      "point 72: temperature 1501.51 K, heat loss 120.15 W/m^2\n",
      "point 73: temperature 1522.79 K, heat loss 122.28 W/m^2\n",
      "point 74: temperature 1542.70 K, heat loss 124.27 W/m^2\n",
      "point 75: temperature 1561.31 K, heat loss 126.13 W/m^2\n",
      "point 76: temperature 1578.67 K, heat loss 127.87 W/m^2\n",
      "point 77: temperature 1594.87 K, heat loss 129.49 W/m^2\n",
      "point 78: temperature 1609.99 K, heat loss 131.00 W/m^2\n",
      "point 79: temperature 1624.11 K, heat loss 132.41 W/m^2\n",
      "point 80: temperature 1637.31 K, heat loss 133.73 W/m^2\n",
      "point 81: temperature 1649.69 K, heat loss 134.97 W/m^2\n",
      "point 82: temperature 1661.30 K, heat loss 136.13 W/m^2\n",
      "point 83: temperature 1672.23 K, heat loss 137.22 W/m^2\n",
      "point 84: temperature 1682.55 K, heat loss 138.25 W/m^2\n",
      "point 85: temperature 1701.50 K, heat loss 140.15 W/m^2\n",
      "point 86: temperature 1718.61 K, heat loss 141.86 W/m^2\n",
      "point 87: temperature 1734.16 K, heat loss 143.42 W/m^2\n",
      "point 88: temperature 1748.37 K, heat loss 144.84 W/m^2\n",
      "point 89: temperature 1761.40 K, heat loss 146.14 W/m^2\n",
      "point 90: temperature 1773.35 K, heat loss 147.34 W/m^2\n",
      "point 91: temperature 1784.33 K, heat loss 148.43 W/m^2\n",
      "point 92: temperature 1803.61 K, heat loss 150.36 W/m^2\n",
      "point 93: temperature 1819.76 K, heat loss 151.98 W/m^2\n",
      "point 94: temperature 1833.11 K, heat loss 153.31 W/m^2\n",
      "point 95: temperature 1844.02 K, heat loss 154.40 W/m^2\n",
      "point 96: temperature 1852.79 K, heat loss 155.28 W/m^2\n",
      "point 97: temperature 1859.75 K, heat loss 155.98 W/m^2\n",
      "point 98: temperature 1865.18 K, heat loss 156.52 W/m^2\n",
      "point 99: temperature 1869.37 K, heat loss 156.94 W/m^2\n",
      "point 100: temperature 1872.54 K, heat loss 157.25 W/m^2\n",
      "point 101: temperature 1874.92 K, heat loss 157.49 W/m^2\n",
      "point 102: temperature 1876.68 K, heat loss 157.67 W/m^2\n",
      "point 103: temperature 1878.93 K, heat loss 157.89 W/m^2\n",
      "point 104: temperature 1880.22 K, heat loss 158.02 W/m^2\n",
      "point 105: temperature 1880.96 K, heat loss 158.10 W/m^2\n",
      "point 106: temperature 1881.39 K, heat loss 158.14 W/m^2\n",
      "point 107: temperature 1881.64 K, heat loss 158.16 W/m^2\n",
      "point 108: temperature 1881.78 K, heat loss 158.18 W/m^2\n",
      "point 109: temperature 1881.91 K, heat loss 158.19 W/m^2\n",
      "point 110: temperature 1881.99 K, heat loss 158.20 W/m^2\n",
      "point 111: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 112: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 113: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 114: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 115: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 116: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 117: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 118: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 119: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 120: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 121: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 122: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 123: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 124: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 125: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 126: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 127: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 128: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 129: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 130: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 131: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 132: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 133: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 134: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 135: temperature 1882.00 K, heat loss 158.20 W/m^2\n",
      "point 136: temperature 1882.00 K, heat loss 158.20 W/m^2\n"
     ]
    }
   ],
   "source": [
    "# set stretch rate and heat loss\n",
    "stretch_rate = 10.0  # 1/s (ex)\n",
    "heat_loss_coefficient = 0.1  # W/m^2·K (ex)\n",
    "\n",
    "# heat loss calculate function regarding to the temperature\n",
    "def calculate_heat_loss(T, T_env):\n",
    "    return heat_loss_coefficient * (T - T_env)\n",
    "\n",
    "# environment temperature\n",
    "T_env = 300  # K (ex)\n",
    "\n",
    "# calculate heat loss\n",
    "for i in range(len(fl1.grid)):\n",
    "    heat_loss = calculate_heat_loss(fl1.T[i], T_env)\n",
    "    print(f'point {i}: temperature {fl1.T[i]:.2f} K, heat loss {heat_loss:.2f} W/m^2')\n",
    "\n",
    "# analyze composition changes\n",
    "phi_values = np.linspace(0.5, 1.5, 5)\n",
    "temperatures = []\n",
    "\n",
    "for phi in phi_values:\n",
    "    gas1.set_equivalence_ratio(phi, fuel, oxidizer)\n",
    "    fl1.set_initial_guess()\n",
    "    fl1.solve(loglevel, auto=True)\n",
    "    temperatures.append(fl1.products.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a10bed9-ea5b-4a30-9358-4b4299014171",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
